Fiber structures are common in material science images, biological and biomedical images; such structures include various engineered fibers, bronchial tree, as well as cytoskeleton and neuronal network. A high volume data of acquired images is used to analyze the connectivity and functionality of these structures. Reliable interpretation and analysis require accurate quantitative measures.
However, extracting useful information accurately and rapidly is challenging because of the variety of imaging conditions and complexity of the structures. Images from different sources can have different noise or uneven background intensities, which eliminates the possibility of having a reliable method of extracting fibers without considering local shape properties of the fibers. In the case of neurites the fibers become smaller and significantly dimmer when they are extended away from the cell, causing most algorithms to produce result with poor quality.
Additionally, it may be a large number of fibers in an image implying that the efficiency of local fiber shape estimation is critical for the processing speed. In the biomedical field, detection of fibers can be more difficult than common engineering material because the image of the tubular structures can be a 2D projection of 3D structures intersecting and branching through the cell.
Therefore a need exists for a method providing highly efficient estimation of local fiber shapes and system therefor, allowing for automated fiber tracing that would meet the accuracy and speed requirements of practical applications.